Water Needs of Cotton Plants under Climate Change in Syria During the Period 1970-2020
Bibliographic record
Abstract
This research aims to study the reality of changes in the climatic water balance and its impact on the water needs of cotton plants in Syria during the period from 1970 to 2020. It evaluates drought in the study area using the NDMI (Normalized Difference Moisture Index) based on 20 cloud-free Landsat satellite images with a spatial resolution of 30m over the study area during the studied period (1970-2020). Additionally, the research analyzes climatic water balance elements such as precipitation and potential evapotranspiration (PET) at annual, seasonal, and monthly levels, while identifying the general trend throughout the study period. It also aims to determine the changes in actual evapotranspiration (AET), which reflects the actual water needs for cotton plants in the main cultivation areas in Syria (Hama, Aleppo, Raqqa, Deir Al-Zor) during the growing season and at each of its four growth stages, within the context of current climate changes. Simple linear regression models were used to identify the trend for precipitation, potential evapotranspiration (PET), actual evapotranspiration (AET), and the climatic water balance. The results indicated a statistically significant general trend ( P < 0.05) for both potential and actual evapotranspiration during the studied period, while showing a statistically significant decreasing trend for precipitation ( P < 0.05) at the selected climate stations, along with the existence of a climatic water deficit in the cotton-growing regions of Syria throughout the studied period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".